The paper introduces OrbitalGym, a JAX‑based simulation environment for studying how planning algorithms and system design influence the outcomes of adversarial orbital encounters. It supports multiple agents, diverse state representations, communication modes, and sensor models, and compiles games to batched GPU code for large‑scale parallel simulation. Using OrbitalGym, the authors evaluate several control policies (PD, LQR, MPC, MCTS) across two close‑range games—pursuit‑evasion and lady‑bandit‑guard—showing that propellant availability often dominates in pursuit‑evasion, while policy choice significantly improves defense in the lady‑bandit‑guard scenario.
By Duncan Eddy, Mahdi Al-Husseini, Mykel J. Kochenderfer
arXiv:2610.41647v1 Announce Type: new
Abstract: Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome vari...
By Adam Abdin (LGI)
The paper presents a chance-constrained belief-space planning framework for autonomous collision avoidance in low Earth orbit. It models uncertain orbital states as Gaussian beliefs and uses a Monte Carlo tree search to decide whether to wait for better tracking data or to execute a maneuver before the time of closest approach. Experiments on 96 scenarios from NASA’s dataset show that the planner can avoid maneuvers in about 40% of cases while keeping collision risk below the threshold, with performance heavily dependent on tracking quality and cadence.
By Grace Ra Kim, Duncan Eddy, Mykel J. Kochenderfer
arXiv:2608. 09628v1 Announce Type: new Abstract: Collision avoidance systems are commonly used to avoid fragmentation events occurring in Low-Earth Orbit (LEO) and Geosynchronous Equatorial Orbit (GEO).
By Logan Luna (Georgia Institute of Technology), Juan Ortiz Couder (Embry-Riddle Aeronautical University), Raul Alejandro Vargas-Acosta (Embry-Riddle Aeronautical University)
The paper introduces Reachability Analysis-Informed Reinforcement Learning (RARL) for designing deterministic multi‑impulse interplanetary transfers. RARL uses local first‑order reachability maps to bound velocity perturbations and selects intermediate waypoints, which are then translated into maneuvers via Lambert reconstruction and a terminal two‑impulse solution. Numerical experiments on an Earth‑Mars benchmark show that RARL achieves a mean maneuver cost only 1.72% above a validated convex programming reference and can be trained once to handle a wide range of departure states, achieving 100% feasibility on 10,000 held‑out Monte Carlo departures.
By Yashdeep Chaudhary, Roberto Armellin, Harry Holt
arXiv:2609.22762v1 Announce Type: new
Abstract: Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expe...
By Fengcheng Yu, Dhruv Parikh, Junjie Ye, Maulik Bhatt, Thang Vu, Igor Vasiljevic, Vitor Guizilini, Yue Wang
The paper compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) for cooperative motion planning of dual‑arm space manipulators tasked with reaching a target while avoiding inter‑arm collisions. A centralized state representation and multi‑objective reward function are used, and SAC consistently outperforms PPO across five random seeds, achieving an 80% success rate within 1.6 M steps versus PPO’s failure to reach this threshold within 4 M steps. Both algorithms degrade on unseen boundary targets, but SAC retains most of its performance while PPO collapses, revealing a reward‑loitering failure mode for PPO.
whyItMatters":"The study demonstrates that off‑policy SAC is more robust and effective than on‑policy PPO for coordinating dual‑arm space robots, informing algorithm choice for active debris removal and on‑orbit servicing tasks."
By Nasim Fallahiarezoodar, Zheng H. Zhu
arXiv:2607. 18200v1 Announce Type: cross Abstract: Robots in cluttered indoor spaces often fail not because they cannot generate collision-free paths, but because a fixed safety margin is mis-calibrated: conservative margins cause detours and timeouts, while permissive margins lead to near-boundary shortcuts under perception bias.
By Junyi Hu, Shuaihang Yuan, Geeta Chandra Raju Bethala, Anthony Tzes, Yi Fang
The paper introduces Exchange Policy Optimization (EPO), a framework for semi‑infinite safe reinforcement learning that handles infinitely many constraints by iteratively solving finite subproblems. EPO expands or deletes constraints based on tolerance violations and Lagrange multipliers, maintaining computational tractability while converging to an optimal policy with bounded safety violations. The authors prove finite convergence, provide iteration bounds, and quantify the suboptimality gap under mild assumptions.
By Jiaming Zhang, Yujie Yang, Haoning Wang, Liping Zhang, Shengbo Eben Li
The paper derives closed‑form bounds on the performance of a drone‑mesh interception system before any learning policy is applied. It shows that, due to a separation constraint, a mesh of agents can cover at most 90.69 % of a plane (74.05 % of a volume) and that optimal hexagonal packing achieves this limit. The authors validate these theoretical ceilings with Monte‑Carlo simulations and argue that such bounds should be reported alongside any learning‑based results.
By Dmytro Babarytskyi
The paper introduces a new approach to learning chance-constrained Markov decision processes (CCMDPs) using a Bellman distributional certificate. It provides both model-based and model-free algorithms with theoretical guarantees, including matching upper and lower bounds for tabular discounted CCMDPs with bounded successor support. Numerical experiments on synthetic CCMDPs and an IEEE 14-bus energy storage benchmark demonstrate the safety and effectiveness of the proposed methods.
By Chenbei Lu, Hongyu Yi
arXiv:2608.24282v1 Announce Type: new
Abstract: Adaptive LiDAR scanning concentrates a limited sensing budget on regions of interest predicted from past object tracks, lowering data volume in autonom...
By Jiachen Gong, Yun Li, Ehsan Javanmardi, Wencan Mao, Manabu Tsukada